Model Parameters Research Articles

Efficacious application of data-driven machine learning models for predicting and optimizing the flexural tensile strength of fiber-reinforced concrete

In the literature, a limited number of studies have utilized the random forest (RF) technique to model the flexural tensile strength (FTS) of steel fiber reinforced concrete (SFRC). This study established modeling of the FTS of SFRC containing mono-fibrous (exclusively crimped and hooked) systems. A comprehensive database of 193 records was used from 18 published studies (including 30 documents sampled by authors). Machine-learning numerical models were developed and evaluated. Partial dependence plots were constructed to visualize the relationship between the model parameters and rank the parameters based on their importance. It has been found that the RF model can be rationally used to predict and optimize the FTS of SFRC, as most of the predicted–tested results were close to the ±80% accuracy range. The model showed of underestimation nature for SFRC with high FTS values. The Gini index-based importance analysis showed that the fiber’s content and tensile strength had the highest significance among the other variables. A drastic increase in the FTS by an increase in the fibrous dosage was achieved. However, no major SFRC’s tensile strength enhancement for dosages was more than 1.5%, while its optimum diameter was 0.5–0.7mm. The results also illustrated that the effect of the fiber’s length is quite similar to that of the fiber content. However, no significant increase can be achieved by increasing the fiber’s length from 40 to 60mm.

Consumption prediction with privacy concern: Application and evaluation of Federated Learning

In this paper, we propose privacy-friendly electricity consumption prediction models based on Federated Learning (FL). Federated Learning provides a novel framework for Artificial Neural Network training. It decouples data storage from model training from decentralized data sources. A central server does not need to collect raw training data from individual clients but only the model parameters, like the gradients or weights updates. The sensitive raw data can be safely stored and used by end-users themselves. We improve the accuracy of the FL model by first clustering households, and training a personalized model for each cluster. We also analyze the Deep Leakage from Gradients (DLG) attack in our study case with three scenarios. Our simulations suggest that the DLG attack can barely succeed in consumption prediction. In this way, the FL model can guarantee clients’ privacy by design. We use a large-scale dataset that contains 3590 households’ 1.5 years of consumption to test the FL model’s performance. Several clustering algorithms are tested for the following experiments. To comprehensively test the FL model’s performance, we propose several popular neural network models: the simple Deep Neural Network, Long Short-Term Memory, Convolutional Neural Network, and WaveNet. These models are both trained under the centralized and federated framework. The federated trained models slightly sacrifice the model’s accuracy while guaranteeing the client’s data privacy. Meanwhile, the federated model shows remarkable scalability. Under the FL framework, new clients can obtain their prediction 6 times faster. Moreover, the federated model has strong robustness against missing or damaged training data. With a certain percentage of missing data in the training set, the centralized model’s accuracy gets 27% worse, while the federated model’s accuracy only gets 2% worse.

Grid-aware Stochastic Game modeling of residential electric flexibility under incentives

We consider a community of households owning electrical assets that can be operated as flexible loads, such as an electric vehicle, a heat pump with thermal storage and a stationary battery. Automated agents are tasked with the management of flexibility under a variable time-of-use tariff with the goal of maximizing final user utility. A population of homogeneous automated agents with similar interests, managing flexibilities of the same nature, runs the risk of stressing the grid through excessively synchronized actions, and this interaction among the agents has to be taken into account. We model this environment as a Stochastic Game, developing a formulation of the state and action spaces and obtaining the transition probability matrix. The probabilities involved are fitted on real or realistic synthetic data. Leveraging the particular structure of the problem, we propose a two-step solution method similar to policy iteration, that proves capable of producing sensible solutions. Finally, we provide an example of flexibility steering through the modulation of the time-of-use tariff, investigating the effectiveness of the incentive depending on its entity and the model parameters that characterize punishment for overcoordination and user satisfaction, and we illustrate how the tool could be employed in a real Energy Community.

An online battery–state of charge estimation method using the varying forgetting factor recursive least square - unscented Kalman filter algorithm on electric vehicles

Accurate and fast estimation of the state of charge is important for the battery management system of electric vehicles. This paper proposes a method to estimate the state of charge of Lithium-ion batteries by the <span lang="EN-US">variable</span> forgetting factor recursive least square (VFFRLS) – unscented Kalman filter (UKF) algorithm in real-time without the off-line battery testing data. Since the state observation requires an accurate model, an equivalent circuit model was constructed. Then, the VFFRLS algorithm is used to identify online the battery model parameters based on voltage and current measurements. An advantage of this algorithm is that it requires less initial information and shorter identification time than offline parameter identification. After the model parameters are well identified, the unscented Kalman filter estimates the state of charge and minimizes noise characteristics and uncertainty in the parameter identification process. The VFFRLS algorithm applied in this paper has shown a good result with the model output error of less than 1%, and the identification achieves real-time response. The state of charge obtained by the UKF algorithm has shown satisfactory estimation results with fast convergence speed and small errors. The UKF filter provides the results with a 1.5% error from the reference and converges after 10 cycles.

Objective Bayesian Meta-Analysis Based on Generalized Marginal Multivariate Random Effects Model

Objective Bayesian inference procedures are derived for the parameters of the multivariate random effects model generalized to elliptically contoured distributions. The posterior for the overall mean vector and the between-study covariance matrix is deduced by assigning two noninformative priors to the model parameter, namely the Berger and Bernardo reference prior and the Jeffreys prior, whose analytical expressions are obtained under weak distributional assumptions. It is shown that the only condition needed for the posterior to be proper is that the sample size is larger than the dimension of the data-generating model, independently of the class of elliptically contoured distributions used in the definition of the generalized multivariate random effects model. The theoretical findings of the paper are applied to real data consisting of ten studies about the effectiveness of hypertension treatment for reducing blood pressure where the treatment effects on both the systolic blood pressure and diastolic blood pressure are investigated.

Blockchain and differential privacy-based data processing system for data security and privacy in urban computing

Recently, big data related to human movement, air quality, and meteorology have been generated in urban computing through sensing technology and the computing infrastructure. However, security problems arise as data utilization increases. If the sensing data from internet of things devices are constantly exposed, the users’ private information can be determined, a critical security risk that could result in privacy breaches. This paper proposes a secure data processing system using the blockchain and differential privacy for data security and privacy protection in urban computing. When a service provider requests information, the system generates it from urban computing data using machine learning. We apply differential privacy to these data to protect privacy. However, if a query repeats, differential privacy may provide insufficient privacy protection. Therefore, we reduce the total privacy cost by reusing noise for the same data and privacy parameters using the blockchain. Machine learning accuracy may decrease when noisy data are used for training. Thus, we increase accuracy by storing and appropriately using the model parameters generated by the same data in the blockchain. We design, simulate, and analyze the results of an experimental environment for reusing noise for differential privacy and parameter utilization of machine learning using the blockchain. The proposed approach reduces privacy costs compared to the existing mechanism while protecting data privacy. We demonstrate that, through parameter utilization, the accuracy improves compared to conventional mechanisms.

An advanced approach for accurate pneumonia detection using combined deep convolutional neural networks

Pneumonia, a lung infection caused by viral or bacterial agents, poses a significant health risk by affecting one or both lungs in humans. Accurate diagnosis, particularly in pediatric cases, is crucial for timely intervention. chest X-rays (CXRs) are a common and non-invasive diagnostic tool to detect pneumonia-related abnormalities. Nonetheless, the minimal radiation exposure suitable for pediatric diagnosis poses a challenge in accurately detecting pneumonia in children. This work proposes a concatenation model that combines two pre-trained convolutional neural networks (CNNs) depending on the transfer learning (TL) technique and optimizes the training parameters to build a highly accurate model for detecting pediatric pneumonia from CXR images. The concatenated extracted features from the two pre-trained CNNs are passed through a convolutional layer to select more valuable semantic features to reduce the extracted features, which helps reduce the model parameters and execution time. Experimental results demonstrate that the feature concatenation technique, along with optimization of training parameters, surpasses the performance of individual CNNs and several state-of-the-art methods. The proposed method achieves a classification accuracy of 98.5%, precision of 99.5%, sensitivity of 98.4%, and F1 score of 99.1%. The primary objective of the proposed approach is to aid radiologists in achieving accurate pneumonia diagnosis in real-time.

A lightweight YOLOv5 for real-time dangerous weapons detection

Deep neural networks are currently employed to detect weapons, and although these techniques provide a high level of accuracy, it still suffers from large weight parameters and a slow inference speed. When it comes to real-world applications, such as weapon detection, these methods are often not suitable for deployment on embedded devices. Because of the huge number of parameters and poor efficiency. The most recent object detection technique, which belongs to the YOLOv5 class, is commonly used for detecting weapons. However, it faces some difficulties such as high computational parameters and an unfavorable detection rate. to solve these shortcomings. an enhanced lightweight Yolov5s approach is suggested. Which consists of a combination of YOLOv5 and GhostNet modules. To evaluate the efficacy of the suggested technique, a set of experiments was performed on the Sohas weapon dataset., which is commonly used as a reference dataset in the field. Compared to the original YOLOv5, the results indicate a slight increase in the proposed model's mean Average Precision (mAP). Furthermore, there has been a reduction of 2.7 in GFLOPs and weights, and the number of model parameters has decreased by 1.42.

Numerical simulation model of water quality evolution in urban rivers considering adsorption–desorption process of nitrogen and phosphorus in sediments

In order to more realistically simulate the river channel water quality considering the characteristics of sediment release, it is necessary to couple the simulation model of sediment release to the hydrology-hydrodynamics-water quality model to quantify the complexities involved in the nitrogen and phosphorus cycle in river channel sediments. However, in the existing studies, most of the water–sediment models constructed in the analysis of single pollutants such as NH3-N or BOD, but fail to consider the interaction between multiple pollutants, and the model software related to water quality simulation fails to establish complex river sediment pollution simulations, there are still challenges in the application of the existing water quality module of the MIKE simulation software to the nitrogen and phosphorus cycle of sediments. In this study, based on the new sediment nitrogen and phosphorus release mechanism, we rewrite the water quality module ECOLab of the MIKE 21 model. The laboratory test results and the measured data are applied to the calibration of the model parameters. The new water quality module considering the adsorption–desorption process of sediments was embedded into the MIKE 21 model. The new module couples the MIKE model and the SWWM model to establish a 2D water quality model of river channel hydrology-hydrodynamics-water quality-sediment release coupling. This model targeted applies in urban river channels with sediment releasion as the main pollution. The results show that the new model has significantly improved the simulation accuracy of BOD and NH3-N, and has an optimized effect on the simulation of TP. The R, NSE, and RMSE values of the model for NH3-N simulation are 0.904, 0.845, and 0.612. This model is more suitable for numerical simulation of urban river water quality where sediment release is the main cause of endogenous pollution.

A framework for the in silico assessment of the robustness of an MPC in a CDC line in function of process variability

The shift from batch manufacturing towards continuous manufacturing for the production of oral solid dosages requires the development and implementation of process models and process control. Previous work focused mainly on developing deterministic models for the investigated system. Furthermore, the in silico tuning and analysis of a control strategy are mostly done based on deterministic models. This deterministic approach could lead to wrong actions in diversion strategies and poor transferability of the controller performance if the system behaves differently than the deterministic model. This work introduces a framework that explicitly includes the process variability which is characteristic of powder handling processes and tests it on a novel continuous feeding-blending unit (i.e., the FE continuous processing system (CPS)), followed by a tablet press (i.e., the FE 55). It employs a stochastic model by allowing the model parameters to have a probability distribution. The performance of a model predictive control (MPC), steering the feed rate of the main excipient feeder to compensate for the feed rate deviations of the active pharmaceutical ingredient (API) feeder to keep the API concentration close to the desired value, is evaluated and the impact of process variability is assessed in a Monte Carlo (MC) analysis. Next to the process variability, a model for the prediction error of the chemometric model and realistic feed rate disturbances were included to increase the transferability of the results to the real system. The obtained results show that process variability is inherently present and that wrong conclusions can be drawn if it is not taken into account in the in silico analysis.

Probing the universe's expansion dynamics: The linear correction scenario perspective on dark energy

In this study, we investigate the cosmological evolution of the universe, focusing on the KMMS models in a model-independent approach, particularly the scenario involving linear correction, given by E2(z)=A(z)+β(1+γB(z)), where A(z)=Ωm0(1+z)3 and B(z)=z. By analyzing recent Cosmic Chronometers (CC) and the Pantheon+ samples, we determine the best-fit values of model parameters using a Markov chain Monte Carlo analysis. Our models exhibit unique dynamics, transitioning from super-exponential to exponential expansion, with a significant shift at redshift zt=0.83−0.10+0.11 and present deceleration parameter q0=−0.69−0.17+0.17. The jerk parameter exceeds 1, indicating a faster change in acceleration than predicted by ΛCDM. The energy density parameter is consistent with Planck observations, and the dark sector evolution follows the expected thermal history. The EoS parameter approaches ωde=−1 over time, with a current value of ωde0=−1.06±0.12, aligning with the phantom phase. Our model presents a new dark energy alternative, emphasizing the importance of considering models like KMMS in understanding cosmic evolution.

A Novel Battery Model Considering the Battery Actual Reaction Mechanism for Model Parameters and SOC Joint Estimation

A Novel Battery Model Considering the Battery Actual Reaction Mechanism for Model Parameters and SOC Joint Estimation

Streamflow prediction in ungauged basins: How dissimilar are drainage basins?

Hydrological processes leading to flow in river channels are extremely complex, which is why we cannot use any fundamental law to fully explain river flow dynamics at basin scale. The general approach for hydrological modelling considers that every catchment’s response to precipitation input is unique, which means different catchments respond differently to the same precipitation input. In other words, river basins are sufficiently dissimilar to warrant having hydrological models with multiple free-parameters for predicting discharge. It is assumed that free-parameters are related to static catchment characteristics. However, past efforts to relate model parameters with observable catchment characteristics have not been very successful. We thus need a framework to quantify catchment dissimilarities exhibited by catchment characteristics because there is no need of free-parameters if we decide to ignore dissimilarities. Thus, relative role of climate and catchment characteristics in streamflow response of the basin needs to be quantified. In this study, we have considered daily data from 3534 gauging stations spread across the globe to compare HBV, a widely used hydrological model, with Dynamic Budyko (DB) model, a recently proposed calibration-free model. The HBV model assumes that both climate and catchment characteristics are important in modeling streamflow response, whereas a calibration-free DB model essentially assumes that streamflow response is largely governed by climate. The performance of HBV run with regionalized parameters and DB in terms of median coefficient of determination (R2) and weighted R2 (WR2) are (0.43, 0.45) and (0.24, 0.27), respectively. The corresponding values of R2 and WR2 from quantile-quantile comparison are (0.94, 0.94) and (0.60, 0.59). Results here seem to support of idea of developing calibration-free models by appreciating similarities between basins, particularly if the objectives are prediction in ungauged basins and assessment of climate change impact on streamflow.

Squaring Circuit Using 14nmFinFET Technology with Vedic Mathematics Approach

FinFET technology is an alternative to CMOS technology as in this technology device has higher drive current, speed, and low-power consumption. FinFET has better mobility and scaling than CMOS technology. A dedicated Squaring circuit is needed by many digital signal processors (DSP) and arithmetic and logical units (ALU). So fast, power-efficient, and compact squaring circuits can be designed with Vedic sutras. Vedic mathematics has 16 sutras and 13 sub-sutras that touch almost all different chapters of mathematics. Out of these sutras, Dwandwayoga has a duplex property that is used to find a square of an N-bit number. Also, Yavadunam is a special case for finding the square of a number that is closer to the base. This work targets a comparative analysis of these two square circuits having a Vedic approach with a conventional array multiplier. The proposed designs are implemented in micro-wind 3.9 electronic design automation tool (EDA) with 14 nm deep submicron technology post-layout. The values of model parameters are used from the current Berkeley 4 short channel model (BSIM4). It is observed that the proposed square architecture design using the Dwandwayog sutra and Yavadunam sutra achieves 185%, 272.45% delay depletion and a 122.22%, and 46.52% reduction in transistor requirements compared to the conventional array multiplier, respectively.

Application of growth models to South African Boer goat castrates and does under feedlot conditions

Mathematical models may aid researchers in describing biological processes, like growth, in animals. This study aimed to collect the body weight data of 18 Boer goat castrates and 20 Boer goat does, from birth until maturity, to model growth and determine growth trends. This is a novel investigation as sufficient information on an age-weight database for these two Boer goat sexes from birth to maturity, is lacking. Using age-weight data, four nonlinear models, namely the Brody, Gompertz, Logistic and Von Bertalanffy growth models, were plotted and evaluated. The model parameters of each growth model were compared for differences between the two sexes. The statistical effectiveness of fit was determined for each model using AIC and RMSE, with R2 also being considered. All models except the Brody model, predicted significantly heavier mature weights for castrates. The Brody model was deemed unfit to describe Boer goat growth as the function severely over-predict weights from birth until maturity for both sexes. The Von Bertalanffy (R2 = 91.3) and Gompertz functions (R2 = 91.3) showed the best fit for Boer goat castrates, while the Gompertz model (R2 = 95.1) showed the best fit for Boer goat does. The Gompertz function is the preferred model to depict Boer goat growth overall, as it accurately characterized growth of both sexes. According to the Gompertz model the age at which the inflection point of the growth curve was reached, did not differ significantly between castrates and does (141.80 days versus 136.31 days). There was also no significant difference in maturation rate between the two sexes.

Uncover Spatially Informed Variations for Single-Cell Spatial Transcriptomics with STew

Abstract Motivation The recent spatial transcriptomics (ST) technologies have enabled characterization of gene expression patterns and spatial information, advancing our understanding of cell lineages within diseased tissues. Several analytical approaches have been proposed for ST data, but effectively utilizing spatial information to unveil the shared variation with gene expression remains a challenge. Results We introduce STew, a Spatial Transcriptomic multi-viEW representation learning method, to jointly analyze spatial information and gene expression in a scalable manner, followed by a data-driven statistical framework to measure the goodness of model fit. Through benchmarking using human dorsolateral prefrontal cortex (DLPFC) and mouse main olfactory bulb (MOB) data with true manual annotations, STew achieved superior performance in both clustering accuracy and continuity of identified spatial domains compared with other methods. STew is also robust to generate consistent results insensitive to model parameters, including sparsity constraints. We next applied STew to various ST data acquired from 10x Visium, Slide-seqV2, and 10x Xenium, encompassing single-cell and multi-cellular resolution ST technologies, which revealed spatially informed cell type clusters and biologically meaningful axes. In particular, we identified a pro-inflammatory fibroblast spatial niche using ST data from psoriatic skins. Moreover, STew scales almost linearly with the number of spatial locations, guaranteeing its applicability to datasets with thousands of spatial locations to capture disease relevant niches in complex tissues. Availability Source code and the R software tool STew are available from github.com/fanzhanglab/STew. Supplementary information Supplementary data are available at Bioinformatics Advances online.

Two-phase data science framework for compensation of the friction force in CNC machines

ABSTRACT This study proposes a two-phase data science framework for the friction force and parameter estimation of the hysteresis effect segment in the servo-control systems of precision machines. The first phase uses an exponential-based friction force model to identify the model parameters by an autoregressive model and Z-transform. The second phase uses symbolic regression for the residual analysis to enhance the friction force estimation. An empirical study of three types of CNC machines under different working conditions is conducted to validate the two-phase data science framework and identify the change of the machining displacement considered to be a critical factor affecting friction force. The exponential-based model successfully eliminates the circular spike error caused by the friction force in the tapping center machine. The results indicate that the proposed two-phase framework improves mean absolute error by 5.6% on average in the tapping center, 6.5% in the milling machine and 7.6% in the turning and milling center, respectively.

Joint Modeling of Gene-Environment Correlations and Interactions Using Polygenic Risk Scores in Case-Control Studies.

Polygenic risk scores (PRS) are rapidly emerging as a way to measure disease risk by aggregating multiple genetic variants. Understanding the interplay of PRS with environmental factors is critical for interpreting and applying PRS in a wide variety of settings. We develop an efficient method for simultaneously modeling gene-environment correlations and interactions using PRS in case control studies. We use a logistic-normal regression modeling framework to specify the disease risk and PRS distribution in the underlying population and propose joint inference across the two models using the retrospective likelihood of the case-control data. Extensive simulation studies demonstrate the flexibility of the method in trading-off bias and efficiency for the estimation of various model parameters compared to the standard logistic regression or a case-only analysis for gene-environment interactions, or a control-only analysis for gene-environment correlations. Finally using simulated case-control data sets within the UK Biobank study, we demonstrate the power of our method for its ability to recover results from the full prospective cohort for the detection of an interaction between long-term oral contraceptive use and PRS on the risk of breast cancer. This method is computationally efficient and implemented in a user-friendly R package.

Trajectory Tracking Control of Unmanned Underwater Vehicle Based on Projected Perpendicular Guidance Method with Disturbance Observer

Unmanned underwater vehicles (UUVs) possess impressive maneuverability and versatility, but controlling them during trajectory tracking can be challenging due to their susceptibility to external disturbances and perturbations in their model parameters. Additionally, the UUV has four degrees of freedom underwater, but only three control inputs, making it a typical underactuated system. To address these issues, this paper introduces a novel optimize sliding mode control (OPSMC) algorithm grounded in projected perpendicular guidance (PPG). The PPG algorithm transforms the three-degree-of-freedom path trajectory control into two-degree-of-freedom heading tracking control and surge velocity tracking control by designing virtual posture angles. Optimized sliding mode control, based on sliding mode control, improves control precision and reduces control input chattering by constructing optimization functions for control inputs. During trajectory tracking, UUVs are susceptible to external environmental disturbances and perturbations in system model parameters. Disturbance observers are employed to estimate these disturbances and perturbations. Finally, MATLAB/Simulink is used for numerical simulation experiments. The simulation results demonstrate that the PPG algorithm effectively enables underactuated UUVs to achieve trajectory tracking control. The designed optimized sliding mode controller and disturbance observer enhance the control precision and robustness of the system.

Valuations of generalized variance swaps under the jump–diffusion model with stochastic liquidity risk

This paper focuses on the pricing problem of generalized variance swaps with jump risk in the underlying asset price under a stochastic liquidity model. We obtain a pricing formula of generalized variance swap in a jump–diffusion model with stochastic liquidity risk by the joint moment generating function (MGF) generated by solving the partial integral differential equation (PIDE). Using asymptotic analysis, we also demonstrate that as the sampling interval approaches zero, the pricing formula of discretely sampled generalized variance swap tends to be that of continuously sampled generalized variance swap. Finally, to verify the feasibility of the pricing formula of the generalized variance swap presented in this paper, we conduct some numerical experiments, including a comparison with the results of Monte Carlo (MC) simulation, the impact of various model parameters on the delivery prices of generalized variance swaps, and empirical research using actual market data.